Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

Each file is in the pickle format and was generated with Python 3.8.5. Each file contains a Python dictionary with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org). Details on saved models coming soon.所有文件均采用pickle格式（pickle）存储，且由Python 3.8.5版本生成。每个文件内含一个Python字典，包含以下字段：训练图像（train_images）：共计64万张float32格式（float32）图像，用于模型训练。其中20px尺寸的图像包含400个像素强度值，40px尺寸的图像则各包含1600个像素强度值。训练标签（train_labels）：对应训练图像集中每张图像的float32格式标签。所有自然图像的标签均为0.0；纹理图像则根据其所属纹理类别，被标记为0.0、1.0、2.0、3.0或4.0。测试图像（test_images）：共计6.4万张float32格式图像，用于模型测试。其中20px尺寸的图像包含400个像素强度值，40px尺寸的图像则各包含1600个像素强度值。测试标签（test_labels）：对应测试图像集中每张图像的float32格式标签。所有自然图像的标签均为0.0；纹理图像则根据其所属纹理类别，被标记为0.0、1.0、2.0、3.0或4.0。如需获取更多细节，请参阅提交至NeurIPS 2022的论文《Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder》，该论文即将在arXiv.org上线发布。已保存模型的相关细节将在后续公布。